The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The Hull Method for Selecting the Number of Common Factors
Open accessAbstract
A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors. The study compares the method with four other methods such as parallel analysis and the minimum average partial test, which were selected because they have been proven to perform well and/or they are frequently used in applied research. The Hull method outperformed all four methods at recovering the correct number of major factors. Its usefulness was further illustrated by its assessment of the dimensionality of the Five-Factor Personality Inventory ( Hendriks, Hofstee, & De Raad, 1999 ). This inventory has 100 items, and the typical methods for assessing dimensionality prove to be useless: the large number of factors they suggest has no theoretical justification. The Hull method, however, suggested retaining the number of factors that the theoretical background to the inventory actually proposes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Costello, A. B., & Osborne, J. (2005) · University of Massachusetts Amherst · doi:10.7275/jyj1-4868 · Cited by 10,297
Scale Development Research: A Content Analysis and Recommendations for Best Practices
Worthington, R. L., & Whittaker, T. A. (2006) · The Counseling Psychologist · doi:10.1177/0011000006288127 · Cited by 4,087
Henson, R. K., & Roberts, J. K. (2006) · Educational and Psychological Measurement · doi:10.1177/0013164405282485 · Cited by 2,454
Determining the number of components from the matrix of partial correlations
Velicer, W. F. (1976) · Psychometrika · doi:10.1007/bf02293557 · Cited by 2,438
The Sources of Four Commonly Reported Cutoff Criteria: What Did They Really Say?
Lance, C. E., Butts, M. M., & Michels, L. C. (2006) · Organizational Research Methods · doi:10.1177/1094428105284919 · Cited by 2,053
Some necessary conditions for common-factor analysis
Guttman, L. (1954) · Psychometrika · doi:10.1007/bf02289162 · Cited by 1,689
Discarding Variables in a Principal Component Analysis. I: Artificial Data
Jolliffe, I. T. (1972) · Applied Statistics · doi:10.2307/2346488 · Cited by 907
Cited by
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00149 · Cited by 5,925
Gámez‐Guadix, M., Almendros, C., Calvete, E., & De Santisteban, P. (2018) · Journal of Adolescence · doi:10.1016/j.adolescence.2017.12.002 · Cited by 76
Lecavalier, L., Bodfish, J., Harrop, C., Whitten, A., Jones, D., Pritchett, J., Faldowski, R., & Boyd, B. (2020) · Autism Research · doi:10.1002/aur.2257 · Cited by 40
Coelho, G. L. H., Vilar, R., Hanel, P. H. P., Monteiro, R. P., Ribeiro, M. G. C., & Gouveia, V. V. (2018) · Personality and Individual Differences · doi:10.1016/j.paid.2018.06.030 · Cited by 38
Gignac, G. E., & Ilić, D. (2025) · Intelligence · doi:10.1016/j.intell.2025.101922 · Cited by 4
Metadata
- Authors
- Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 2011-04-11
- Volume
- 46
- Issue
- 2
- Pages
- 340-364
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- OpenAlex
- W2117197890
- Cited by
- 564
- References
- 9
- FWCI
- 12.57
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
